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Cerebrospinal Fluid and Plasma Pharmacokinetics of the Cyclooxygenase 2 Inhibitor Rofecoxib in Humans: Single and Multiple Oral Drug Administration

2005· article· en· W2034096429 on OpenAlexaff
Asokumar Buvanendran, Jeffrey S. Kroin, Kenneth J. Tuman, Timothy R. Lubenow, Dalia Elmofty, Pauline Luk

Bibliographic record

VenueAnesthesia & Analgesia · 2005
Typearticle
Languageen
FieldMedicine
TopicInflammatory mediators and NSAID effects
Canadian institutionsMerck Canada Inc. (Canada)
Fundersnot available
KeywordsRofecoxibPharmacokineticsCerebrospinal fluidMedicineDosingPharmacologyCyclooxygenaseArea under the curveAnalgesicAnesthesiaInternal medicineChemistry

Abstract

fetched live from OpenAlex

In Brief Cerebrospinal fluid (CSF) pharmacokinetics of orally administered cyclooxygenase 2 inhibitors, with single or multiple dosing, is of clinical relevance because it may relate to the analgesic efficacy of these drugs. We enrolled 9 subjects with implanted intrathecal catheters in the study. After 50-mg oral rofecoxib administration, the CSF drug concentration lagged slightly behind the plasma drug concentration. The ratio of the 24-h area under the drug-concentration curve (AUC) in CSF to plasma was 0.142. After daily dosing of rofecoxib 50 mg/d for 9 days, rofecoxib concentrations in plasma and CSF were larger on Day 9 than on Day 1, with the 24-h AUC on Day 9 more than twice the Day 1 AUC for both plasma and CSF. After nine consecutive daily doses of rofecoxib, the AUCCSF/AUCplasma ratio was 0.159. The important findings of this study are that CSF rofecoxib levels are approximately 15% of plasma levels and that repeated daily dosing more than doubles the AUC in CSF. IMPLICATIONS: When the selective cyclooxygenase 2 inhibitor rofecoxib is given orally at 50 mg, the cerebrospinal fluid concentration of the drug is 15% of the plasma concentration. Daily dosing to reach steady-state more than doubles the amount of rofecoxib in the cerebrospinal fluid.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.193
Threshold uncertainty score0.584

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.243
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations29
Published2005
Admission routes1
Has abstractyes

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